Recent research suggests that the most effective treatment of suicidal states occurs in the context of multi- level prevention initiatives within healthcare delivery systems. Such findings underscore the importance to create services that can address suicide and reinjury risks. While offering key services have been associated with reduced suicide rates, it is important to understand how services contribute to prevent self-harming persons from subsequent reinjury. For instance, the 2012 National Action Alliance for Suicide Prevention Research Task Force Stakeholder Survey ranked studies of suicide and suicide reinjury among its three highest priorities for research. Presently, up to 25% of all suicide completers and up to 70% of all nonfatal reinjuring patients are treated in a healthcare environment for a known nonfatal act of self-harm within 12 months of subsequent reinjury. Despite the availability of multi-state, population-based, patient-level, and system-level data, th information in such databases is not being effectively extracted. We wish to demonstrate that advanced statistical modeling will provide mechanisms for the extraction of such information that will enable policy makers to differentiate healthcare delivery systems that are more effective in preventing intentional reinjury from those that experience higher reinjury rates. This Phase I study investigates the feasibility of applying new statistical methods to analyze models of care for self-harming individuals using patient-level medical records obtained from AHRQ's extensive Healthcare Cost and Utilization Project [HCUP]. Data characterizing consecutive presentations to healthcare systems for treatment of self-harm from the state health services divisions or state hospital associations in five states will be used first to develop healthcare system risk models and then to test associations between post-injury medical treatment and the likelihood of fatal or nonfatal intentional reinjury within 12 months of an index, nonfatal intentional self-injury. Model will be developed using the Best Approximating Model (BAM) approach that selects and validates robust model searches while simultaneously handling common modeling problems such as the presence of possible model misspecification, missing values, over fitting, multicollinearity, rare event outcomes bias, and inflated error from multiple comparisons. Simulation studies will be performed to characterize the advantages of the BAM strategy for developing a robust suicide/reinjury risk model over widely-used multivariate statistical methods such as stepwise regression. Feasibility study results will provide the preliminary research for more advanced Phase II healthcare risk model development, evaluation, and dissemination that, in turn, establishes the essential foundation for Phase III product commercialization.